### Table 5: Execution Times of Asynchronous Genetic Algorithm and Distributed Asynchronous Genetic Algorithm on CM-5. (The unit is second and the numbers in parenthesis are the speedups.)

"... In PAGE 30: ... Moreover, in this scheme there is no central processing to bottleneck performance since all genetic operations are carried out by processors autonomously with at most minimal exchange with a randomly chosen partner. The bene cial e ect is shown in Table5 which compares exper- imental results for the DAGA with the earlier scheme applied to the same problem of optimizing a fuzzy controller design for an inverted pendulum [2]. Note that while the original scheme apos;s per- formance does not scale with increasing processors, the distributed version achieves quite close to... ..."

### Table 2. Genetic Algorithm Results: Running Times.

2000

"... In PAGE 17: ... 4.3 Computation Times Table2 gives information about running times for each of the trials. The columns are as follows: Problem: As in Table 1.... ..."

Cited by 4

### Table 2. Genetic Algorithm Results: Running Times.

2000

"... In PAGE 17: ... 4.3 Computation Times Table2 gives information about running times for each of the trials. The columns are as follows: Problem: As in Table 1.... ..."

Cited by 4

### Table 1 Quantum Monte Carlo (QMC) algorithms for fermion systems, their main areas of applications, and available approximate approaches to overcome the sign decay and exponential scaling of the sign problem. The new method removes `??? apos;.

"... In PAGE 2: ... The new method makes possible systematic studies of the temperature dependence of correlated-electron models without the exponential growth of computer time with inverse temperature. In Table1 we list the commonly applied QMC methods for fermion systems and some of their application areas, partly to put the new method in context, and partly to highlight the underlying connections between the various meth- ods. Methods in the second column are based in real (con guration) space and use rst-quantized representation, while those in the last column are based in auxiliary- eld space and use second-quantized representation.... In PAGE 2: ... Their application areas have not over- lapped a great deal[5]; the \real-space quot; methods have mostly been applied to continuum systems, while the auxiliary- eld methods more to lattice systems. The new method removes the set of question marks in Table1 and enables, for the rst time, nite-T simulations of correlated electron models with favorable computational scaling. It provides the nite-T counterpart of the T = 0 K constrained path Monte Carlo (CPMC) method developed several years ago, which has seen a variety of applications[14] to systems ranging from models for high-Tc to zeolites to organic superconductors.... ..."

### Table 20: Genetic Algorithm Solution, Problem 3 Tubing Diameter

"... In PAGE 9: ... Genetic Algorithm Solution This problem was solved with the genetic algorithm one time. The solution is described in Table20 . To reach this solution, 3,120 function calls were required.... ..."

### Table 4. Advantages and drawbacks of genetic and greedy algorithms.

"... In PAGE 11: ... The execution time is the main drawback of the genetic algorithm approach since it takes into account more alternatives than the greedy algorithm. Table4 sum- marizes the features of genetic and greedy algorithms in the TAN minimization problem. Table 4.... ..."

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### Table 2: Results for hybrid genetic on Drexx instances

"... In PAGE 19: ... PIII 500MHz] 10000 100000 TS iterations 500 550 600 650 700 750 800 Solution value Confidence Scatter Search GHS(100) Figure 8: Comparison of Genetic hybrid short search, GHS (Drezner 2002b, c) with parallel scatter search (Cung and Donadio 2002) for problem instance Dre30. In Table2 , we report computational results obtained by the compounded hybrid genetic algorithm for different Drexx instances. Each instance was solved 20 times.... In PAGE 19: ...ection 3.1.3. Table2 provides best and average solution values (absolute and relative to the optimum). Examining this table, we conclude that the difficulty of the instances rapidly grows with their size.... In PAGE 19: ... The experimental time complexity of our method grows as O(n4). The results in Table2 are quite good. However, when compared with other instances of the QAPLIB of similar sizes (see Drezner, 2002c) it is clear that these instances are much more difficult.... ..."

### Table 4: Genetic Algorithm Performance on External Simulator

1999

"... In PAGE 34: ... PLACE TABLE 4 HERE We next looked at the 50 best solutions and 50 randomly selected so- lutions in the hybrid genetic algorithm population at 60, 70, 80 and 100 thousand evaluations using the external simulator. The results presented in Table4 suggest the solutions found at 60 thousand evaluations are basically as good as the solutions at 100 thousand evaluations. The best solution ever seen on this problem as evaluated by the external simulator is found in the randomly selected set after 70,000 evaluations: a mean time at dock of 378.... ..."

Cited by 10

### Table 4: Genetic Algorithm Performance on External Simulator

1999

"... In PAGE 34: ... PLACE TABLE 4 HERE We next looked at the 50 best solutions and 50 randomly selected so- lutions in the hybrid genetic algorithm population at 60, 70, 80 and 100 thousand evaluations using the external simulator. The results presented in Table4 suggest the solutions found at 60 thousand evaluations are basically as good as the solutions at 100 thousand evaluations. The best solution ever seen on this problem as evaluated by the external simulator is found in the randomly selected set after 70,000 evaluations: a mean time at dock of 378.... ..."

Cited by 10

### Table 1: Relative Performance of the genetic algorithm (OCH) * with respect to the genetic algorithm R-Key

1995

"... In PAGE 10: ...02. As we can see from the computational results in Table1 , R-Key was unable to find the optimum solution for most of the cases, but performed reasonably well on most test problems. In implementing R-Key , we ran each genetic algorithm three times and selected the best of the three solutions.... ..."